This e-Research adaptive user interface (eRaUI) project is aiming at developing a personalized user interfaces for a text mining e-Research tool called NaCTeM. eRaUI will be adaptable to different usages and different level of researchers’ knowledge and preferences increasing the use of NaCTeM e-research tools by making it easier to learn and adaptable to the requirements of different user groups.
Project Team: Prof. Farhi Marir, Dr. Sahithi Siva and Dr. Yanguo Jing

Thursday, 8 September 2011

Ideas

The following features could be integrated into eRaUI (when running in widget form) to enhance the user experience.

Social Networking Icons – i.e. facebook and twitter (like / tweet)

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Feedback and bug reporting capabilities

Existing Softwares which place a widget (toolbar) within the browser window:

·The most simple and feasible mechanism to deploy a browser-based widget is probably to add a javascript include (single line of code) to the section of a webpage.

·Another possibility is to create a Wibiya Application – allowing eRaUI to be deployed inside Wibiya.

·Could also feasibly be run as a Browser Extension (like the Google Toolbar) so that it could work with any website or web application.

Algorithms and methods for machine learning

Machine learning algorithms could be used to determine which features of eRaUI are being used on a website and which are redundant. Thus the application could switch features on and off according to the extent to which they are made use of. This might be particularly useful when taking into account detected User-Agent or resolving a client’s IP address to their geographic location. Machine learning algorithms could be used to determine what elements of eRaUI are best for presenting to the user.

Types of Machine Learning Algorithms:

·Supervised Learning – less suitable because of the need for an element of human supervision.

·Semi-Supervised Learning – same issues as Supervised Learning in terms of requiring an element of human supervision.

·Reinforcement Learning – possible

·Genetic Algorithms – it may be possible to allow the underlying algorithms to modify themselves in such a way as to present the most useful information to the user.